Construct Non-Hierarchical P/NBD Model for Online Retail Transaction Data
In this workbook we construct our first hierarchical P/NBD models on the synthetic data with the longer timeframe.
1 Load and Construct Datasets
We start by modelling the P/NBD model using our synthetic datasets before we try to model real-life data.
Show code
use_fit_start_date <- as.Date("2009-12-01")
use_fit_end_date <- as.Date("2010-12-01")
use_valid_start_date <- as.Date("2010-12-01")
use_valid_end_date <- as.Date("2011-12-31")1.1 Load Online Retail Data
We now want to load the short time-frame synthetic data.
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customer_cohortdata_tbl <- read_rds("data/onlineretail_cohort_tbl.rds")
customer_cohortdata_tbl |> glimpse()Rows: 5,852
Columns: 5
$ customer_id <chr> "12346", "12347", "12348", "12349", "12350", "12351", …
$ cohort_qtr <chr> "2010 Q1", "2010 Q4", "2010 Q3", "2010 Q2", "2011 Q1",…
$ cohort_ym <chr> "2010 03", "2010 10", "2010 09", "2010 04", "2011 02",…
$ first_tnx_date <date> 2010-03-02, 2010-10-31, 2010-09-27, 2010-04-29, 2011-…
$ total_tnx_count <int> 3, 8, 5, 3, 1, 1, 9, 2, 1, 2, 6, 2, 5, 10, 6, 4, 10, 2…Show code
customer_transactions_tbl <- read_rds("data/onlineretail_invoice_cleaned_tbl.rds")
customer_transactions_tbl |> glimpse()Rows: 36,594
Columns: 4
$ invoice_date <date> 2009-12-01, 2009-12-01, 2009-12-01, 2009-12-01, 2009-12…
$ customer_id <chr> "12490", "12533", "12682", "12758", "12836", "12913", "1…
$ invoice_id <chr> "489557", "489526", "489439", "489599", "489593", "48954…
$ invoice_spend <dbl> 531.94, 821.92, 372.30, 2454.68, 423.87, 537.96, 261.00,…1.2 Load Derived Data
Show code
customer_summarystats_tbl <- read_rds("data/onlineretail_customer_summarystats_tbl.rds")
obs_fitdata_tbl <- read_rds("data/onlineretail_obs_fitdata_tbl.rds")
obs_validdata_tbl <- read_rds("data/onlineretail_obs_validdata_tbl.rds")
customer_fit_stats_tbl <- obs_fitdata_tbl |>
rename(x = tnx_count)2 Fit First P/NBD Model
We now construct our Stan model and prepare to fit it with our synthetic dataset.
Before we start on that, we set a few parameters for the workbook to organise our Stan code.
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stan_modeldir <- "stan_models"
stan_codedir <- "stan_code"We also want to set a number of overall parameters for this workbook
To start the fit data, we want to use the 1,000 customers. We also need to calculate the summary statistics for the validation period.
2.1 Compile and Fit Stan Model
We now compile this model using CmdStanR.
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pnbd_fixed_stanmodel <- cmdstan_model(
"stan_code/pnbd_fixed.stan",
include_paths = stan_codedir,
pedantic = TRUE,
dir = stan_modeldir
)We then use this compiled model with our data to produce a fit of the data.
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stan_modelname <- "pnbd_onlineretail_fixed1"
stanfit_seed <- stanfit_seed + 1
stanfit_prefix <- str_c("fit_", stan_modelname)
stanfit_object_file <- glue("data/{stanfit_prefix}_stanfit.rds")
stan_data_lst <- customer_fit_stats_tbl |>
select(customer_id, x, t_x, T_cal) |>
compose_data(
lambda_mn = 0.25,
lambda_cv = 1.00,
mu_mn = 0.10,
mu_cv = 1.00,
)
if(!file_exists(stanfit_object_file)) {
pnbd_onlineretail_fixed1_stanfit <- pnbd_fixed_stanmodel$sample(
data = stan_data_lst,
chains = 4,
iter_warmup = 500,
iter_sampling = 500,
seed = stanfit_seed,
save_warmup = TRUE,
output_dir = stan_modeldir,
output_basename = stanfit_prefix,
)
pnbd_onlineretail_fixed1_stanfit$save_object(stanfit_object_file, compress = "gzip")
} else {
pnbd_onlineretail_fixed1_stanfit <- read_rds(stanfit_object_file)
}
pnbd_onlineretail_fixed1_stanfit$summary()# A tibble: 12,718 × 10
variable mean median sd mad q5 q95 rhat ess_bulk
<chr> <num> <num> <num> <num> <num> <num> <num> <num>
1 lp__ -6.82e+4 -6.82e+4 71.9 71.7 -6.83e+4 -6.81e+4 1.01 704.
2 lambda[1] 6.43e-2 5.20e-2 0.0497 0.0409 1.04e-2 1.59e-1 1.00 1962.
3 lambda[2] 1.41e-1 9.80e-2 0.148 0.0972 6.83e-3 4.24e-1 1.00 1817.
4 lambda[3] 1.33e-1 8.00e-2 0.160 0.0884 4.79e-3 4.41e-1 1.00 1351.
5 lambda[4] 5.78e-2 4.77e-2 0.0408 0.0350 1.11e-2 1.37e-1 1.00 1626.
6 lambda[5] 2.47e-1 1.61e-1 0.271 0.174 1.03e-2 7.39e-1 1.00 1200.
7 lambda[6] 3.09e-1 2.59e-1 0.216 0.185 5.64e-2 7.29e-1 1.00 2131.
8 lambda[7] 1.42e-1 9.21e-2 0.155 0.0979 7.57e-3 4.44e-1 1.00 1678.
9 lambda[8] 1.35e-1 7.92e-2 0.161 0.0886 4.57e-3 4.50e-1 1.00 1374.
10 lambda[9] 2.70e-1 2.45e-1 0.154 0.140 7.44e-2 5.52e-1 1.00 1920.
# ℹ 12,708 more rows
# ℹ 1 more variable: ess_tail <num>We have some basic HMC-based validity statistics we can check.
Show code
pnbd_onlineretail_fixed1_stanfit$cmdstan_diagnose()Processing csv files: /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed1-1.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed1-2.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed1-3.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed1-4.csvWarning: non-fatal error reading adaptation data
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.2.2 Visual Diagnostics of the Sample Validity
Now that we have a sample from the posterior distribution we need to create a few different visualisations of the diagnostics.
Show code
parameter_subset <- c(
"lambda[1]", "lambda[2]", "lambda[3]", "lambda[4]",
"mu[1]", "mu[2]", "mu[3]", "mu[4]"
)
pnbd_onlineretail_fixed1_stanfit$draws(inc_warmup = FALSE) |>
mcmc_trace(pars = parameter_subset) +
expand_limits(y = 0) +
labs(
x = "Iteration",
y = "Value",
title = "Traceplot of Sample of Lambda and Mu Values"
) +
theme(axis.text.x = element_text(size = 10))
We also check \(N_{eff}\) as a quick diagnostic of the fit.
Show code
pnbd_onlineretail_fixed1_stanfit |>
neff_ratio(pars = c("lambda", "mu")) |>
as.numeric() |>
mcmc_neff() +
ggtitle("Plot of Parameter Effective Sample Sizes")
2.3 Assess the Model
As we intend to run the same logic to assess each of our models, we have combined all this logic into a single function run_model_assessment, to run the simulations and combine the datasets.
Show code
pnbd_onlineretail_fixed1_assess_data_lst <- run_model_assessment(
model_stanfit = pnbd_onlineretail_fixed1_stanfit,
insample_tbl = customer_fit_stats_tbl,
outsample_tbl = customer_valid_stats_tbl,
fit_label = "pnbd_onlineretail_fixed1",
fit_end_dttm = use_fit_end_date |> as.POSIXct(),
valid_start_dttm = use_valid_start_date |> as.POSIXct(),
valid_end_dttm = use_valid_end_date |> as.POSIXct(),
sim_seed = 10
)
pnbd_onlineretail_fixed1_assess_data_lst |> glimpse()List of 5
$ model_fit_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed1_assess_fit_index_tbl.rds"
$ model_valid_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed1_assess_valid_index_tbl.rds"
$ model_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed1_assess_model_simstats_tbl.rds"
$ model_fit_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed1_assess_fit_simstats_tbl.rds"
$ model_valid_simstats_filepath: 'glue' chr "data/pnbd_onlineretail_fixed1_assess_valid_simstats_tbl.rds"2.3.1 Check In-Sample Data Validation
We first check the model against the in-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed1_assess_data_lst |>
use_series(model_fit_simstats_filepath) |>
read_rds()
insample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_fitdata_tbl,
simdata_tbl = simdata_tbl
)
insample_plots_lst$multi_plot |> print()
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insample_plots_lst$total_plot |> print()
Show code
insample_plots_lst$quant_plot |> print()
This fit looks reasonable and appears to capture most of the aspects of the data used to fit it. Given that this is a synthetic dataset, this is not surprising, but at least we appreciate that our model is valid.
2.3.2 Check Out-of-Sample Data Validation
We now repeat for the out-of-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed1_assess_data_lst |>
use_series(model_valid_simstats_filepath) |>
read_rds()
outsample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_validdata_tbl,
simdata_tbl = simdata_tbl
)
outsample_plots_lst$multi_plot |> print()
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outsample_plots_lst$total_plot |> print()
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outsample_plots_lst$quant_plot |> print()
As for our short time frame data, overall our model is working well.
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3771669 201.5 6554501 350.1 6554501 350.1
Vcells 83250000 635.2 256750661 1958.9 256749976 1958.93 Fit Alternate Prior Model.
We want to try an alternate prior model with a smaller co-efficient of variation to see what impact it has on our procedures.
Show code
stan_modelname <- "pnbd_onlineretail_fixed2"
stanfit_seed <- stanfit_seed + 1
stanfit_prefix <- str_c("fit_", stan_modelname)
stanfit_object_file <- glue("data/{stanfit_prefix}_stanfit.rds")
stan_data_lst <- customer_fit_stats_tbl |>
select(customer_id, x, t_x, T_cal) |>
compose_data(
lambda_mn = 0.25,
lambda_cv = 0.50,
mu_mn = 0.10,
mu_cv = 0.50,
)
if(!file_exists(stanfit_object_file)) {
pnbd_onlineretail_fixed2_stanfit <- pnbd_fixed_stanmodel$sample(
data = stan_data_lst,
chains = 4,
iter_warmup = 500,
iter_sampling = 500,
seed = stanfit_seed,
save_warmup = TRUE,
output_dir = stan_modeldir,
output_basename = stanfit_prefix,
)
pnbd_onlineretail_fixed2_stanfit$save_object(stanfit_object_file, compress = "gzip")
} else {
pnbd_onlineretail_fixed2_stanfit <- read_rds(stanfit_object_file)
}
pnbd_onlineretail_fixed2_stanfit$summary()# A tibble: 12,718 × 10
variable mean median sd mad q5 q95 rhat ess_bulk
<chr> <num> <num> <num> <num> <num> <num> <num> <num>
1 lp__ -1.44e+5 -1.44e+5 67.2 66.7 -1.44e+5 -1.44e+5 1.00 719.
2 lambda[1] 1.32e-1 1.21e-1 0.0625 0.0582 4.95e-2 2.51e-1 1.00 3089.
3 lambda[2] 2.11e-1 1.93e-1 0.108 0.0999 7.49e-2 4.12e-1 1.00 2902.
4 lambda[3] 2.07e-1 1.86e-1 0.108 0.0955 7.00e-2 4.12e-1 1.00 3208.
5 lambda[4] 1.09e-1 1.02e-1 0.0475 0.0431 4.30e-2 1.99e-1 1.00 3086.
6 lambda[5] 2.49e-1 2.26e-1 0.129 0.119 7.89e-2 4.93e-1 1.00 4277.
7 lambda[6] 2.69e-1 2.50e-1 0.119 0.113 1.09e-1 4.97e-1 1.00 3642.
8 lambda[7] 2.09e-1 1.90e-1 0.109 0.103 6.30e-2 4.09e-1 1.00 3500.
9 lambda[8] 2.12e-1 1.96e-1 0.109 0.101 6.60e-2 4.20e-1 1.01 3648.
10 lambda[9] 2.60e-1 2.44e-1 0.107 0.103 1.12e-1 4.61e-1 1.01 3962.
# ℹ 12,708 more rows
# ℹ 1 more variable: ess_tail <num>We have some basic HMC-based validity statistics we can check.
Show code
pnbd_onlineretail_fixed2_stanfit$cmdstan_diagnose()Processing csv files: /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed2-1.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed2-2.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed2-3.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed2-4.csvWarning: non-fatal error reading adaptation data
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.3.1 Visual Diagnostics of the Sample Validity
Now that we have a sample from the posterior distribution we need to create a few different visualisations of the diagnostics.
Show code
parameter_subset <- c(
"lambda[1]", "lambda[2]", "lambda[3]", "lambda[4]",
"mu[1]", "mu[2]", "mu[3]", "mu[4]"
)
pnbd_onlineretail_fixed2_stanfit$draws(inc_warmup = FALSE) |>
mcmc_trace(pars = parameter_subset) +
expand_limits(y = 0) +
labs(
x = "Iteration",
y = "Value",
title = "Traceplot of Sample of Lambda and Mu Values"
) +
theme(axis.text.x = element_text(size = 10))
We want to check the \(N_{eff}\) statistics also.
Show code
pnbd_onlineretail_fixed2_stanfit |>
neff_ratio(pars = c("lambda", "mu")) |>
as.numeric() |>
mcmc_neff() +
ggtitle("Plot of Parameter Effective Sample Sizes")
3.2 Assess the Model
As we intend to run the same logic to assess each of our models, we have combined all this logic into a single function run_model_assessment, to run the simulations and combine the datasets.
Show code
pnbd_onlineretail_fixed2_assess_data_lst <- run_model_assessment(
model_stanfit = pnbd_onlineretail_fixed2_stanfit,
insample_tbl = customer_fit_stats_tbl,
outsample_tbl = customer_valid_stats_tbl,
fit_label = "pnbd_onlineretail_fixed2",
fit_end_dttm = use_fit_end_date |> as.POSIXct(),
valid_start_dttm = use_valid_start_date |> as.POSIXct(),
valid_end_dttm = use_valid_end_date |> as.POSIXct(),
sim_seed = 20
)
pnbd_onlineretail_fixed2_assess_data_lst |> glimpse()List of 5
$ model_fit_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed2_assess_fit_index_tbl.rds"
$ model_valid_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed2_assess_valid_index_tbl.rds"
$ model_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed2_assess_model_simstats_tbl.rds"
$ model_fit_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed2_assess_fit_simstats_tbl.rds"
$ model_valid_simstats_filepath: 'glue' chr "data/pnbd_onlineretail_fixed2_assess_valid_simstats_tbl.rds"3.2.1 Check In-Sample Data Validation
We first check the model against the in-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed2_assess_data_lst |>
use_series(model_fit_simstats_filepath) |>
read_rds()
insample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_fitdata_tbl,
simdata_tbl = simdata_tbl
)
insample_plots_lst$multi_plot |> print()
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insample_plots_lst$total_plot |> print()
Show code
insample_plots_lst$quant_plot |> print()
This fit looks reasonable and appears to capture most of the aspects of the data used to fit it. Given that this is a synthetic dataset, this is not surprising, but at least we appreciate that our model is valid.
3.2.2 Check Out-of-Sample Data Validation
We now repeat for the out-of-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed2_assess_data_lst |>
use_series(model_valid_simstats_filepath) |>
read_rds()
outsample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_validdata_tbl,
simdata_tbl = simdata_tbl
)
outsample_plots_lst$multi_plot |> print()
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outsample_plots_lst$total_plot |> print()
Show code
outsample_plots_lst$quant_plot |> print()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3797211 202.8 6554501 350.1 6554501 350.1
Vcells 134254213 1024.3 308180793 2351.3 308179829 2351.34 Fit Tight-Lifetime Model
We now want to try a model where we use priors with a tighter coefficient of variation for lifetime but keep the CoV for transaction frequency.
Show code
stan_modelname <- "pnbd_onlineretail_fixed3"
stanfit_seed <- stanfit_seed + 1
stanfit_prefix <- str_c("fit_", stan_modelname)
stanfit_object_file <- glue("data/{stanfit_prefix}_stanfit.rds")
stan_data_lst <- customer_fit_stats_tbl |>
select(customer_id, x, t_x, T_cal) |>
compose_data(
lambda_mn = 0.25,
lambda_cv = 1.00,
mu_mn = 0.10,
mu_cv = 0.50,
)
if(!file_exists(stanfit_object_file)) {
pnbd_onlineretail_fixed3_stanfit <- pnbd_fixed_stanmodel$sample(
data = stan_data_lst,
chains = 4,
iter_warmup = 500,
iter_sampling = 500,
seed = stanfit_seed,
save_warmup = TRUE,
output_dir = stan_modeldir,
output_basename = stanfit_prefix,
)
pnbd_onlineretail_fixed3_stanfit$save_object(stanfit_object_file, compress = "gzip")
} else {
pnbd_onlineretail_fixed3_stanfit <- read_rds(stanfit_object_file)
}
pnbd_onlineretail_fixed3_stanfit$summary()# A tibble: 12,718 × 10
variable mean median sd mad q5 q95 rhat ess_bulk
<chr> <num> <num> <num> <num> <num> <num> <num> <num>
1 lp__ -1.12e+5 -1.12e+5 69.6 69.7 -1.12e+5 -1.12e+5 1.00 780.
2 lambda[1] 7.15e-2 5.77e-2 0.0542 0.0429 1.15e-2 1.79e-1 1.00 2655.
3 lambda[2] 1.49e-1 9.94e-2 0.162 0.105 7.06e-3 4.46e-1 1.00 1844.
4 lambda[3] 1.34e-1 8.13e-2 0.158 0.0858 6.35e-3 4.43e-1 1.00 2267.
5 lambda[4] 5.92e-2 5.00e-2 0.0415 0.0377 9.93e-3 1.39e-1 1.00 2612.
6 lambda[5] 2.36e-1 1.69e-1 0.228 0.166 1.38e-2 6.98e-1 1.00 2197.
7 lambda[6] 3.00e-1 2.55e-1 0.208 0.187 5.53e-2 7.02e-1 1.00 2496.
8 lambda[7] 1.44e-1 9.25e-2 0.160 0.0985 6.70e-3 4.23e-1 1.00 2262.
9 lambda[8] 1.34e-1 8.35e-2 0.154 0.0923 5.13e-3 4.34e-1 1.00 2776.
10 lambda[9] 2.69e-1 2.38e-1 0.158 0.144 6.92e-2 5.62e-1 1.00 2091.
# ℹ 12,708 more rows
# ℹ 1 more variable: ess_tail <num>We have some basic HMC-based validity statistics we can check.
Show code
pnbd_onlineretail_fixed3_stanfit$cmdstan_diagnose()Processing csv files: /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed3-1.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed3-2.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed3-3.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed3-4.csvWarning: non-fatal error reading adaptation data
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.4.1 Visual Diagnostics of the Sample Validity
Now that we have a sample from the posterior distribution we need to create a few different visualisations of the diagnostics.
Show code
parameter_subset <- c(
"lambda[1]", "lambda[2]", "lambda[3]", "lambda[4]",
"mu[1]", "mu[2]", "mu[3]", "mu[4]"
)
pnbd_onlineretail_fixed3_stanfit$draws(inc_warmup = FALSE) |>
mcmc_trace(pars = parameter_subset) +
expand_limits(y = 0) +
labs(
x = "Iteration",
y = "Value",
title = "Traceplot of Sample of Lambda and Mu Values"
) +
theme(axis.text.x = element_text(size = 10))
We want to check the \(N_{eff}\) statistics also.
Show code
pnbd_onlineretail_fixed3_stanfit |>
neff_ratio(pars = c("lambda", "mu")) |>
as.numeric() |>
mcmc_neff() +
ggtitle("Plot of Parameter Effective Sample Sizes")
4.2 Assess the Model
As we intend to run the same logic to assess each of our models, we have combined all this logic into a single function run_model_assessment, to run the simulations and combine the datasets.
Show code
pnbd_onlineretail_fixed3_assess_data_lst <- run_model_assessment(
model_stanfit = pnbd_onlineretail_fixed3_stanfit,
insample_tbl = customer_fit_stats_tbl,
outsample_tbl = customer_valid_stats_tbl,
fit_label = "pnbd_onlineretail_fixed3",
fit_end_dttm = use_fit_end_date |> as.POSIXct(),
valid_start_dttm = use_valid_start_date |> as.POSIXct(),
valid_end_dttm = use_valid_end_date |> as.POSIXct(),
sim_seed = 30
)
pnbd_onlineretail_fixed3_assess_data_lst |> glimpse()List of 5
$ model_fit_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed3_assess_fit_index_tbl.rds"
$ model_valid_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed3_assess_valid_index_tbl.rds"
$ model_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed3_assess_model_simstats_tbl.rds"
$ model_fit_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed3_assess_fit_simstats_tbl.rds"
$ model_valid_simstats_filepath: 'glue' chr "data/pnbd_onlineretail_fixed3_assess_valid_simstats_tbl.rds"4.2.1 Check In-Sample Data Validation
We first check the model against the in-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed3_assess_data_lst |>
use_series(model_fit_simstats_filepath) |>
read_rds()
insample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_fitdata_tbl,
simdata_tbl = simdata_tbl
)
insample_plots_lst$multi_plot |> print()
Show code
insample_plots_lst$total_plot |> print()
Show code
insample_plots_lst$quant_plot |> print()
This fit looks reasonable and appears to capture most of the aspects of the data used to fit it. Given that this is a synthetic dataset, this is not surprising, but at least we appreciate that our model is valid.
4.2.2 Check Out-of-Sample Data Validation
We now repeat for the out-of-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed3_assess_data_lst |>
use_series(model_valid_simstats_filepath) |>
read_rds()
outsample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_validdata_tbl,
simdata_tbl = simdata_tbl
)
outsample_plots_lst$multi_plot |> print()
Show code
outsample_plots_lst$total_plot |> print()
Show code
outsample_plots_lst$quant_plot |> print()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3821935 204.2 6554501 350.1 6554501 350.1
Vcells 185256404 1413.4 443956341 3387.2 369895832 2822.15 Fit Narrow-Short-Lifetime Model
We now want to try a model where we use priors with a tighter coefficient of variation for lifetime but keep the CoV for transaction frequency.
Show code
stan_modelname <- "pnbd_onlineretail_fixed4"
stanfit_seed <- stanfit_seed + 1
stanfit_prefix <- str_c("fit_", stan_modelname)
stanfit_object_file <- glue("data/{stanfit_prefix}_stanfit.rds")
stan_data_lst <- customer_fit_stats_tbl |>
select(customer_id, x, t_x, T_cal) |>
compose_data(
lambda_mn = 0.25,
lambda_cv = 1.00,
mu_mn = 0.20,
mu_cv = 0.30,
)
if(!file_exists(stanfit_object_file)) {
pnbd_onlineretail_fixed4_stanfit <- pnbd_fixed_stanmodel$sample(
data = stan_data_lst,
chains = 4,
iter_warmup = 500,
iter_sampling = 500,
seed = stanfit_seed,
save_warmup = TRUE,
output_dir = stan_modeldir,
output_basename = stanfit_prefix,
)
pnbd_onlineretail_fixed4_stanfit$save_object(stanfit_object_file, compress = "gzip")
} else {
pnbd_onlineretail_fixed4_stanfit <- read_rds(stanfit_object_file)
}
pnbd_onlineretail_fixed4_stanfit$summary()# A tibble: 12,718 × 10
variable mean median sd mad q5 q95 rhat ess_bulk
<chr> <num> <num> <num> <num> <num> <num> <num> <num>
1 lp__ -1.85e+5 -1.85e+5 73.2 72.6 -1.85e+5 -1.85e+5 1.01 508.
2 lambda[1] 8.12e-2 6.75e-2 0.0574 0.0499 1.64e-2 1.92e-1 1.00 2576.
3 lambda[2] 1.62e-1 1.09e-1 0.182 0.115 4.14e-3 5.07e-1 1.00 1526.
4 lambda[3] 1.61e-1 1.02e-1 0.183 0.109 6.79e-3 5.06e-1 1.00 2034.
5 lambda[4] 6.06e-2 5.05e-2 0.0427 0.0379 1.06e-2 1.45e-1 1.00 2772.
6 lambda[5] 2.37e-1 1.65e-1 0.231 0.170 1.36e-2 7.13e-1 1.00 2523.
7 lambda[6] 3.02e-1 2.53e-1 0.205 0.182 5.74e-2 6.85e-1 0.999 2449.
8 lambda[7] 1.61e-1 1.09e-1 0.172 0.114 7.40e-3 5.07e-1 1.00 1919.
9 lambda[8] 1.65e-1 1.05e-1 0.182 0.114 7.23e-3 5.39e-1 1.00 2233.
10 lambda[9] 2.70e-1 2.42e-1 0.152 0.135 7.81e-2 5.74e-1 1.00 2380.
# ℹ 12,708 more rows
# ℹ 1 more variable: ess_tail <num>We have some basic HMC-based validity statistics we can check.
Show code
pnbd_onlineretail_fixed4_stanfit$cmdstan_diagnose()Processing csv files: /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed4-1.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed4-2.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed4-3.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed4-4.csvWarning: non-fatal error reading adaptation data
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.5.1 Visual Diagnostics of the Sample Validity
Now that we have a sample from the posterior distribution we need to create a few different visualisations of the diagnostics.
Show code
parameter_subset <- c(
"lambda[1]", "lambda[2]", "lambda[3]", "lambda[4]",
"mu[1]", "mu[2]", "mu[3]", "mu[4]"
)
pnbd_onlineretail_fixed4_stanfit$draws(inc_warmup = FALSE) |>
mcmc_trace(pars = parameter_subset) +
expand_limits(y = 0) +
labs(
x = "Iteration",
y = "Value",
title = "Traceplot of Sample of Lambda and Mu Values"
) +
theme(axis.text.x = element_text(size = 10))
We want to check the \(N_{eff}\) statistics also.
Show code
pnbd_onlineretail_fixed4_stanfit |>
neff_ratio(pars = c("lambda", "mu")) |>
as.numeric() |>
mcmc_neff() +
ggtitle("Plot of Parameter Effective Sample Sizes")
5.2 Assess the Model
As we intend to run the same logic to assess each of our models, we have combined all this logic into a single function run_model_assessment, to run the simulations and combine the datasets.
Show code
pnbd_onlineretail_fixed4_assess_data_lst <- run_model_assessment(
model_stanfit = pnbd_onlineretail_fixed4_stanfit,
insample_tbl = customer_fit_stats_tbl,
outsample_tbl = customer_valid_stats_tbl,
fit_label = "pnbd_onlineretail_fixed4",
fit_end_dttm = use_fit_end_date |> as.POSIXct(),
valid_start_dttm = use_valid_start_date |> as.POSIXct(),
valid_end_dttm = use_valid_end_date |> as.POSIXct(),
sim_seed = 40
)
pnbd_onlineretail_fixed4_assess_data_lst |> glimpse()List of 5
$ model_fit_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed4_assess_fit_index_tbl.rds"
$ model_valid_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed4_assess_valid_index_tbl.rds"
$ model_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed4_assess_model_simstats_tbl.rds"
$ model_fit_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed4_assess_fit_simstats_tbl.rds"
$ model_valid_simstats_filepath: 'glue' chr "data/pnbd_onlineretail_fixed4_assess_valid_simstats_tbl.rds"5.2.1 Check In-Sample Data Validation
We first check the model against the in-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed4_assess_data_lst |>
use_series(model_fit_simstats_filepath) |>
read_rds()
insample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_fitdata_tbl,
simdata_tbl = simdata_tbl
)
insample_plots_lst$multi_plot |> print()
Show code
insample_plots_lst$total_plot |> print()
Show code
insample_plots_lst$quant_plot |> print()
This fit looks reasonable and appears to capture most of the aspects of the data used to fit it. Given that this is a synthetic dataset, this is not surprising, but at least we appreciate that our model is valid.
5.2.2 Check Out-of-Sample Data Validation
We now repeat for the out-of-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed4_assess_data_lst |>
use_series(model_valid_simstats_filepath) |>
read_rds()
outsample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_validdata_tbl,
simdata_tbl = simdata_tbl
)
outsample_plots_lst$multi_plot |> print()
Show code
outsample_plots_lst$total_plot |> print()
Show code
outsample_plots_lst$quant_plot |> print()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3846672 205.5 6554504 350.1 6554504 350.1
Vcells 236258635 1802.6 532827609 4065.2 443955954 3387.26 Compare Model Outputs
We have looked at each of the models individually, but it is also worth looking at each of the models as a group.
Show code
calculate_simulation_statistics <- function(file_rds) {
simdata_tbl <- read_rds(file_rds)
multicount_cust_tbl <- simdata_tbl |>
filter(sim_tnx_count > 0) |>
count(draw_id, name = "multicust_count")
totaltnx_data_tbl <- simdata_tbl |>
count(draw_id, wt = sim_tnx_count, name = "simtnx_count")
simstats_tbl <- multicount_cust_tbl |>
inner_join(totaltnx_data_tbl, by = "draw_id")
return(simstats_tbl)
}Show code
obs_fit_customer_count <- obs_fitdata_tbl |>
filter(tnx_count > 0) |>
nrow()
obs_valid_customer_count <- obs_validdata_tbl |>
filter(tnx_count > 0) |>
nrow()
obs_fit_total_count <- obs_fitdata_tbl |>
pull(tnx_count) |>
sum()
obs_valid_total_count <- obs_validdata_tbl |>
pull(tnx_count) |>
sum()
obs_stats_tbl <- tribble(
~assess_type, ~name, ~obs_value,
"fit", "multicust_count", obs_fit_customer_count,
"fit", "simtnx_count", obs_fit_total_count,
"valid", "multicust_count", obs_valid_customer_count,
"valid", "simtnx_count", obs_valid_total_count
)
model_assess_tbl <- dir_ls("data", regexp = "pnbd_onlineretail_fixed.*_assess_.*simstats") |>
enframe(name = NULL, value = "file_path") |>
filter(str_detect(file_path, "_assess_model_", negate = TRUE)) |>
mutate(
model_label = str_replace(file_path, "data/pnbd_onlineretail_(.*?)_assess_.*", "\\1"),
assess_type = if_else(str_detect(file_path, "_assess_fit_"), "fit", "valid"),
sim_data = map(
file_path, calculate_simulation_statistics,
.progress = "calculate_simulation_statistics"
)
)
model_assess_tbl |> glimpse()Rows: 8
Columns: 4
$ file_path <fs::path> "data/pnbd_onlineretail_fixed1_assess_fit_simstats_tb…
$ model_label <chr> "fixed1", "fixed1", "fixed2", "fixed2", "fixed3", "fixed3"…
$ assess_type <chr> "fit", "valid", "fit", "valid", "fit", "valid", "fit", "va…
$ sim_data <list> [<tbl_df[2000 x 3]>], [<tbl_df[2000 x 3]>], [<tbl_df[2000…We have now constructed the simulation summary statistics and now reshape our data to aid in our model assessment.
Show code
model_assess_summstat_tbl <- model_assess_tbl |>
select(model_label, assess_type, sim_data) |>
unnest(sim_data) |>
pivot_longer(
cols = !c(model_label, assess_type, draw_id)
) |>
group_by(model_label, assess_type, name) |>
summarise(
.groups = "drop",
mean_val = mean(value),
p10 = quantile(value, 0.10),
p25 = quantile(value, 0.25),
p50 = quantile(value, 0.50),
p75 = quantile(value, 0.75),
p90 = quantile(value, 0.90)
)
model_assess_summstat_tbl |> glimpse()Rows: 16
Columns: 9
$ model_label <chr> "fixed1", "fixed1", "fixed1", "fixed1", "fixed2", "fixed2"…
$ assess_type <chr> "fit", "fit", "valid", "valid", "fit", "fit", "valid", "va…
$ name <chr> "multicust_count", "simtnx_count", "multicust_count", "sim…
$ mean_val <dbl> 2594.3130, 12441.8265, 1886.9515, 13200.3765, 2745.4525, 1…
$ p10 <dbl> 2556.0, 12113.9, 1853.0, 12790.9, 2707.0, 9987.9, 1417.0, …
$ p25 <dbl> 2574.0, 12268.0, 1870.0, 12980.0, 2725.0, 10131.0, 1434.0,…
$ p50 <dbl> 2595.0, 12442.0, 1887.0, 13206.5, 2746.0, 10282.0, 1450.0,…
$ p75 <dbl> 2614.00, 12608.25, 1905.00, 13414.50, 2766.00, 10422.25, 1…
$ p90 <dbl> 2632.0, 12771.0, 1919.1, 13604.0, 2784.0, 10562.0, 1483.0,…We now use this data to construct model comparison plots for the different models we have fit.
Show code
#! echo: TRUE
ggplot(model_assess_summstat_tbl) +
geom_errorbar(
aes(x = model_label, ymin = p10, ymax = p90), width = 0
) +
geom_errorbar(
aes(x = model_label, ymin = p25, ymax = p75), width = 0, linewidth = 3
) +
geom_hline(
aes(yintercept = obs_value),
data = obs_stats_tbl, colour = "red"
) +
scale_y_continuous(labels = label_comma()) +
expand_limits(y = 0) +
facet_wrap(
vars(assess_type, name), scale = "free_y"
) +
labs(
x = "Model",
y = "Count",
title = "Comparison Plot for the Different Models"
) +
theme(
axis.text.x = element_text(angle = 20, vjust = 0.5, size = 8)
)
6.1 Write Assessment Data to Disk
We now want to save the assessment data to disk.
Show code
model_assess_tbl |> write_rds("data/assess_data_pnbd_onlineretail_fixed_tbl.rds")7 R Environment
Show code
options(width = 120L)
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.2.3 (2023-03-15)
os Ubuntu 22.04.2 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Dublin
date 2023-07-14
pandoc 2.19.2 @ /usr/local/bin/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
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tibble * 3.2.1 2023-03-20 [1] RSPM (R 4.2.0)
tidybayes * 3.0.4 2023-03-14 [1] RSPM (R 4.2.0)
tidyr * 1.3.0 2023-01-24 [1] RSPM (R 4.2.0)
tidyselect 1.2.0 2022-10-10 [1] RSPM (R 4.2.0)
tidyverse * 2.0.0 2023-02-22 [1] RSPM (R 4.2.0)
timechange 0.2.0 2023-01-11 [1] RSPM (R 4.2.0)
tzdb 0.3.0 2022-03-28 [1] RSPM (R 4.2.0)
utf8 1.2.3 2023-01-31 [1] RSPM (R 4.2.0)
vctrs 0.6.2 2023-04-19 [1] RSPM (R 4.2.0)
vroom 1.6.1 2023-01-22 [1] RSPM (R 4.2.0)
withr 2.5.0 2022-03-03 [1] RSPM (R 4.2.0)
xfun 0.38 2023-03-24 [1] RSPM (R 4.2.0)
xtable 1.8-4 2019-04-21 [1] RSPM (R 4.2.0)
xts 0.13.1 2023-04-16 [1] RSPM (R 4.2.0)
yaml 2.3.7 2023-01-23 [1] RSPM (R 4.2.0)
zoo 1.8-12 2023-04-13 [1] RSPM (R 4.2.0)
[1] /usr/local/lib/R/site-library
[2] /usr/local/lib/R/library
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options(width = 80L)